A Coevolution Approach for Database Schemas and Related Ontologies

Connecting scientific databases is a challenging task which can be supported by ontologies describing them on a semantical level. Unfortunately, ontologies for databases are rarely used, because schemas of research databases change rapidly while the ontologies grow as well. Maintaining the connection between both is time consuming manual work. Therefore it is worthwhile to reduce the required work by automating tasks. We propose an approach that allows the database schema and the ontology to change and evolve, without ever losing their connection to each other. This is done by mapping database schemas to ontologies, enriching these ontologies with semantical information and transfering schema changes to the ontology.

Purpose – Most literature on workflow (WF) adaptation considered the control flow correctness like absence of dead lock, live-lock, etc. during adaptation. The data aspect of WF adaptation like data flow, database schema changes and their correctness are less studied. When the WF schema is modified, their data flow and the database schema changes. The existing approaches used for adapting these data changes in the underlying database schema are time consuming and/or affect the old data persistence. The purpose of this paper is to concern the dynamic adaptation of the WF schema and implementing its data changes in the existing database schema. Design/methodology/approach – A conceptual framework developed to adapt on-the-fly, the concomitant data changes during WF adaptation. The framework consists a set of data schema compliance criteria (DSC) which identify the data changes that can be directly accommodated in the existing database schema. Data adaptation algorithm (DAA) is developed to handle the data changes that does not conform to the DSC in the existing database schema. Findings – In this approach the existing database schema is dynamically evolved without re-creating it, after WF schema adaptation. Therefore the WF schema changes can be implemented on-the-fly without stopping the running system. It also ensures the persistence of old data residing in the existing database. Originality/value – A novel approach developed to adapt the data changes in the existing database schema, without requiring recreation or migration the data. This automated consistency checking of data attribute changes in the database schema and implement them dynamically.

When a web service or application evolves, its database schema --- tables, constraints, and indices --- often need to evolve along with it. Depending on the database, some of these changes require a full table lock, preventing the service from accessing the tables under change. To deal with this, web services are typically taken offline momentarily to modify the database schema. However with the introduction of concepts like Continuous Deployment, web services are deployed into their production environments every time the source code is modified. Having to take the service offline --- potentially several times a day --- to perform schema changes is undesirable. In this paper we introduce QuantumDB--- a tool-supported approach that abstracts this evolution process away from the web service without locking tables. This allows us to redeploy a web service without needing to take it offline even when a database schema change is necessary. In addition QuantumDB puts no restrictions on the method of deployment, supports schema changes to multiple tables using changesets, and does not subvert foreign key constraints during the evolution process. We evaluate QuantumDB by applying 19 synthetic and 95 industrial evolution scenarios to our open source implementation of QuantumDB. These experiments demonstrate that QuantumDB realizes zerodowntime migrations at the cost of acceptable overhead, and is applicable in industrial continuous deployment contexts.

Schema mappings are high-level specifications that describe the relationship between two database schemas. They are considered to be the essential building blocks in data exchange and data integration, and have been the focus of extensive research investigations. Since in real-life applications schema mappings can be quite complex, it is important to develop methods and tools for illustrating, explaining, and deriving schema mappings. A promising approach to this effect is to use "good" data examples that illustrate the schema mapping at hand.

Supporting database schema evolution represents a long-standing challenge of practical and theoretical importance for modern information systems. In this paper, we describe techniques and systems for automating the critical tasks of migrating the database and rewriting the legacy applications. In addition to labor saving, the benefits delivered by these advances are many and include reliable prediction of outcome, minimization of downtime, system-produced documentation, and support for archiving, historical queries, and provenance. The PRISM/PRISM++ system delivers these benefits, by solving the difficult problem of automating the migration of databases and the rewriting of queries and updates. In this paper, we present the PRISM/PRISM++ system and the novel technology that made it possible. In particular, we focus on the difficult and previously unsolved problem of supporting legacy queries and updates under schema and integrity constraints evolution. The PRISM/PRISM++ approach consists in providing the users with a set of SQL-based Schema Modification Operators (SMOs), which describe how the tables in the old schema are modified into those in the new schema. In order to support updates, SMOs are extended with integrity constraints modification operators. By using recent results on schema mapping, the paper (i) characterizes the impact on integrity constraints of structural schema changes, (ii) devises representations that enable the rewriting of updates, and (iii) develop a unified approach for query and update rewriting under constraints. We complement the system with two novel tools: the first automatically collects and provides statistics on schema evolution histories, whereas the second derives equivalent sequences of SMOs from the migration scripts that were used for schema upgrades. These tools were used to produce an extensive testbed containing 15 evolution histories of scientific databases and web information systems, providing over 100 years of aggregate evolution histories and almost 2,000 schema evolution steps.

The objects model (or object oriented [OO] model) is a conceptual-application model that is used to define a database schema representing a certain reality. The model views the world as consisting of objects belonging to classes. The objects of these classes have attributes, behavior (i.e., functions), and various relationships with other objects. The objects model can be presented as a class diagram (also termed OO diagram or objects diagram). Like an entity relationship diagram (ERD), the class diagram has two main goals: 1. To serve as a communication medium between the developers (analysts/ designers) and the users or their representatives. The diagram is created as a result of the interactions between the two parties, during which they discover and define the users’ information needs; the diagram serves like a contract between these two sides which summarizes the users’ needs. 2. To be the basis for further development of the information system (IS). Based on the diagram, it should be possible to design the database schema of the application, and (partially) the functions that it will have to perform. For that, it is necessary to transform the class diagram into an equivalent verbal description—an objects schema. This is done using an object definition language (ODL), similar to data definition language (DDL) in the relational model. In principle, all components of the class diagram are mapped to the objects schema. However, the objects schema includes more details which are not included in the diagram. For example, in the diagram each attribute has a name, and some attributes may have specific constraint definitions (e.g., key, unique); in the objects schema there are more detailed definitions, including the attributes’ domains or data types (e.g., numeric, char., real, etc.) and lengths. Another example, in the class diagram, we only write the names of the classes’ functions, while in the objects schema we specify the parameters of the functions. As aforementioned, there is a great deal of similarity between the OO and ER models and diagrams, since the ER model is one of the sources from which the objects model originated. But there are differences between the two models, which we will review later on. One of these differences is that the ER model is “static,” that is, it only deals with the data structure, while the objects model also includes “behavior,” that is, the functions that operate on the data. The rest of this chapter is dedicated to describing the components of the objects model and the class diagram. The description is organized in four main categories: objects and classes, attributes, relationships, and functions.

Integration of large heterogeneous data collections, gathered usually for making decisions on information security (IS) management issues, requires a preliminary step -- unification of their data models. It is provided by mapping the source data models into the canonical information model. Information and semantics of data definition languages (DDL) and semantics of the operations of data manipulation languages (DML) have to be preserved by the mapping. This research is devoted to the unification of the graph data models (GDM) -- the important kind of the existing various data models. The distinguishing features of modern GDM are discussed as well as their application in the information security (IS) area. The issues of proof of DDL and DML semantics preserving by the mapping of the GDM into the object-frame canonical model are briefly considered. Future work steps in applying research results to different IS management areas are indicated in conclusion.

When a database is shared by many users, updates to the database schema are almost always prohibited because there is a risk of making existing application programs obsolete when they run against the modified schema. The paper addresses the problem by integrating schema evolution with view facilities. When new requirements necessitate schema updates for a particular user, then the user specifies schema changes to his personal view, rather than to the shared base schema. Our view schema evolution approach then computes a new view schema that reflects the semantics of the desired schema change, and replaces the old view with the new one. We show that our system provides the means for schema change without affecting other views (and thus without affecting existing application programs). The persistent data is shared by different views of the schema, i.e., both old as well as newly developed applications can continue to interoperate. The paper describes a solution approach of realizing the evolution mechanism as a working system, which as its key feature requires the underlying object oriented view system to support capacity augmenting views. We present algorithms that implement the complete set of typical schema evolution operations as view definitions. Lastly, we describe the transparent schema evolution system (TSE) that we have built on top of GemStone, including our solution for supporting capacity augmenting view mechanisms.

In the conceptual design of relational databases one of the main goals is to create a conceptual scheme, which minimize redundancies and eliminate deletion and addition anomalies, i.e. to create relation schemes in some good normal form. The study of relational databases has produced a host of normal forms: 2NF, 3NF, BCNF, Elementary-Key Normal Form, 4NF, Weak 4NF, PJ/NF, DK/NF, LTKNF, (3,3)NF, etc. There are two features which characterize these normal forms. First, they consider each relation separately. We believe that a normal form (which reflects the goodness of the conceptual design) should be related to the whole conceptual scheme. Second, the usefullness of all normal forms in relational database design have been based on the assumption that a data definition language (DDL) of a database management system (DBMS) is able to enforce key dependencies. However, different DDLs have different capabilities in defining constraints. In this paper we will discuss the design of conceptual relational schemes in general. We will also define a good normal form (GNF) which requires a minimally rich DDL; this normal form is based only on a primitive concept of constraints. We will not, however, discuss the normalization process itself – how one might, if possible, convert a relation scheme that is not in some normal form into a collection of relation schemes each of which is in that normal form.

Chapter 4 - Basic Data Definition Language (DDL)

The present study examined what type of lexical information could be extracted from the patient's neglected field. Patients (N =32) and age-matched controls (N =32) were required to name a foveally presented word after a parafoveal prime was presented either to the left or right visual field. Primes were identical to the target (KITE-KITE), related to the target at the level of semantics (DOG-CAT), orthography (LEMON-DEMON), phonology (ACHE-LAKE), or both orthography and phonology (HIDE-RIDE). Only when the target was semantically related to the prime were consistently reliable priming effects found in the patients, even in the patients' neglected visual field. These results demonstrate that semantic information can be extracted from the patients' neglected field.

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

Spatial frequency-based information plays an important role in visual perception. By combining behavioral and electroencephalogram (EEG) measurements, we investigated the mechanisms of the interaction and information integration between different spatial frequency bands. The observers performed a scene categorization task on hybrid images that were generated by combining the low spatial frequency (LSF) component of one image with the high spatial frequency (HSF) component of another image. The results showed that the recognition of the HSF component was interfered by the non-attended LSF component at semantic level. The strength of the semantic interference was modulated by the physical similarity between the LSF and HSF components. Analyses of the EEG data revealed an early anterior N1 component (122ms from stimulus onset) that was related to the observed interaction of the semantic and physical information between the LSF and HSF components. These findings demonstrate that the semantic information from different spatial frequency bands can be integrated at early stage of the perceptual processing. This early integration is likely to occur at frontal areas in order to initiate top-down facilitation.

In this paper, we study the integration of functional logic programming and databases by presenting a data model, and a query and data definition language. The data model is adopted from functional logic programming by allowing complex values. The query and data definition language is based on the use of algebra expressions built from a set of algebra operators over an extended relational algebra. In addition, algebra expressions can be used for defining functions, typical in a functional logic program.

The development of new techniques and the emergence of new high- throughput tools have led to a new information revolution. The amount and the diversity of the information that need to be stored and processed have led to the adoption of data integration systems in order to deal with information extraction from disparate sources. The mediation between traditional databases and ontologies has been recognized as a cornerstone issue in bringing in legacy data with formal semantic meaning. However, our knowledge evolves due to the rapid scientific development, so ontologies and schemata need to change in order to capture and accommodate such an evolution. When ontologies change, these changes should somehow be rendered and used by the pre-existing data integration systems, a problem that most of the integration systems seem to ignore. In this paper, we review existing approaches for ontology/schema evolution and examine their applicability in a state-of-the-art, ontology-based data integration setting. Then, we show that changes in schemata differ significantly from changes in ontologies. This strengthens our position that current state of the art systems are not adequate for ontology-based data integration. So, we give the requirements for an ideal data integration system that will enable and exploit ontology evolution.KeywordsOntology EvolutionData IntegrationMappingsEvolution of Mappings